Prior Work Research Articles

Update to the design and methods of the PATH Study, Wave 4 (2016–2017)

The Population Assessment of Tobacco and Health (PATH) Study is a nationally representative study of the US population on tobacco use and its effects on health, with four waves of...

Cross-cultural comparison of the neural correlates of true and false memory retrieval

ABSTRACT Prior work has shown Americans have higher levels of memory specificity than East Asians. Neuroimaging studies have not investigated mechanisms that account for cultural differences at retrieval. In this study, we use fMRI to assess whether mnemonic discrimination, distinguishing novel from previously encountered stimuli, accounts for cultural differences in memory. Fifty-five American and 55 Taiwanese young adults completed an object recognition paradigm testing discrimination of old targets, similar lures and novel foils. Mnemonic discrimination was tested by comparing discrimination of similar lures from studied targets, and results showed the relationship between activity in right fusiform gyrus and behavioural discrimination between target and lure objects differed across cultural groups. Parametric modulation analyses of activity during lure correct rejections also indicated that groups differed in left superior parietal cortex response to variations in lure similarity. Additional analyses of old vs. new activity indicated that Americans and Taiwanese differ in the neural activity supporting general object recognition in the hippocampus, left inferior frontal gyrus and middle frontal gyrus. Results are juxtaposed against comparisons of the regions activated in common across the two cultures. Overall, Americans and Taiwanese differ in the extent to which they recruit visual processing and attention modulating brain regions.

Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study.

Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties. Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their 3- to 5-year-old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist). We found that machine learning models of prenatal proteomics predict 5%-10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes. Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell-signaling pathways were enriched in relation to child temperament and psychopathology measures.

RadVolViz: An Information Display-Inspired Transfer Function Editor for Multivariate Volume Visualization.

In volume visualization transfer functions are widely used for mapping voxel properties to color and opacity. Typically, volume density data are scalars which require simple 1D transfer functions to achieve this mapping. If the volume densities are vectors of three channels, one can straightforwardly map each channel to either red, green or blue, which requires a trivial extension of the 1D transfer function editor. We devise a new method that applies to volume data with more than three channels. These types of data often arise in scientific scanning applications, where the data are separated into spectral bands or chemical elements. Our method expands on prior work in which a multivariate information display, RadViz, was fused with a radial color map, in order to visualize multi-band 2D images. In this work, we extend this joint interface to blended volume rendering. The information display allows users to recognize the presence and value distribution of the multivariate voxels and the joint volume rendering display visualizes their spatial distribution. We design a set of operators and lenses that allow users to interactively control the mapping of the multivariate voxels to opacity and color. This enables users to isolate or emphasize volumetric structures with desired multivariate properties. Furthermore, it turns out that our method also enables more insightful displays even for RGB data. We demonstrate our method with three datasets obtained from spectral electron microscopy, high energy X-ray scanning, and atmospheric science.

Image Collage on Arbitrary Shape via Shape-Aware Slicing and Optimization.

Image collage is a very useful tool for visualizing an image collection. Most of the existing methods and commercial applications for generating image collages are designed on simple shapes, such as rectangular and circular layouts. This greatly limits the use of image collages in some artistic and creative settings. Although there are some methods that can generate irregularly-shaped image collages, they often suffer from severe image overlapping and excessive blank space. This prevents such methods from being effective information communication tools. In this article, we present a shape slicing algorithm and an optimization scheme that can create image collages of arbitrary shapes in an informative and visually pleasing manner given an input shape and an image collection. To overcome the challenge of irregular shapes, we propose a novel algorithm, called Shape-Aware Slicing, which partitions the input shape into cells based on medial axis and binary slicing tree. Shape-Aware Slicing,which is designed specifically for irregular shapes, takes human perception and shape structure into account to generate visually pleasing partitions. Then, the layout is optimized by analyzing input images with the goal of maximizing the total salient regions of the images. To evaluate our method, we conduct extensive experiments and compare our results against previous work. The evaluations show that our proposed algorithm can efficiently arrange image collections on irregular shapes and create visually superior results than prior work and existing commercial tools.

The other side of the coin: An integrative review connecting pay and health.

The organizational sciences have long been interested in the effects of various compensation strategies, and on enhancing employee health. Research examining the connection between pay and health, however, remains a relative rarity. The work that has been done is scattered across disparate disciplines and lacks a unified framework for systematically exploring the effects of pay on health. We argue that greater insecurity at work, as well as rising discontent over wages and work conditions, necessitates a richer understanding of the ways in which organizational pay affects employee psychological, physiological, and behavioral health. We first conduct a comprehensive review of existing research across a broad range of disciplines, taking note of the different ways that pay is conceptualized and the impact it has on employee health. We identify critical knowledge gaps in why and when pay is related to health, noting several disciplinary trends. Drawing on prominent theories of occupational health, we then build a theoretical framework that illustrates three mechanisms underlying the effect of pay on health. We further advance prior work by integrating allostatic load theory to explain how pay gets "under the skin" to affect health, while also identifying relevant moderators and boundary conditions. Taken together, our review integrates findings from a variety of disciplines and facilitates knowledge building across these fields to generate a more comprehensive understanding of the connection between pay and health. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Plant-soil microbe feedbacks depend on distance and ploidy in a mixed cytotype population of Larrea tridentata.

Theory predicts that mixed ploidy populations should be short-lived due to strong fitness disadvantages for the rare ploidy. However, mixed ploidy populations are common, suggesting that the fitness costs for rare ploidies are counterbalanced by ecological benefits that emerge when rare. We investigated whether differences in ecological interactions with soil microbes help to maintain a tetraploid-hexaploid population of Larrea tridentata (creosote bush) in the Sonoran Desert, California, United States, where prior work documented ploidy-specific root-associated microbes. We used a plant-soil feedback (PSF) experiment to test whether host-specific soil microbes can alter the outcomes of intraploidy vs. interploidy competition. Host-specific soil microbes can build up over time; thus, distance from a host plant can affect the fitness of nearby plants. Seedlings grown in soils from near plants of a different ploidy produced greater biomass relative to seedlings grown in soils from near plants of the same ploidy. Moreover, seedlings grown in soils from near plants of a different ploidy produced more biomass than those grown in soils that were farther from plants of a different ploidy. These results suggest that the ecological consequences of PSF may facilitate the persistence of mixed ploidy populations. This is the first evidence, to our knowledge, that is consistent with plant-soil microbe feedback as a viable mechanism to maintain the coexistence of multiple ploidy levels in a single population.

The emotion filmmaker: Temporal memory, time-emotion integration, and affective style.

Emotional experiences are temporally dynamic, but prior work suggests that temporal features are usually neglected in remembered emotion. For instance, retrospective emotion evaluations are often biased by discrete salient timepoints, such as the peak and end moments, at the expense of objective event duration (i.e., peak-end effects and duration neglect). However, how these retrospective emotion biases originate, as well as their significance for emotional functioning, remain unclear. Here, we test the hypothesis that retrospective emotion biases are related to fundamental limits of temporal processing and memory capacity. Further, we examine whether these limits have implications for emotional functioning. Participants (n = 60) underwent a novel paradigm comprising affectively-rich movie sequences while providing emotion ratings continuously (moment-by-moment) and retrospectively. Temporal memory for previously watched emotional movie sequences and dispositional negativity were measured. Our findings revealed a greater "end" bias as the duration of emotional-movie sequences increased, suggesting that limitations in temporal processing capacity may contribute to retrospective emotion biases. Critically, temporal-memory errors were associated with larger retrospective emotion biases and with individual differences in dispositional negativity. Collectively, these results indicate that retrospective emotion biases may stem partly from mnemonic temporal errors that are emotionally maladaptive. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Examining student profiles for dealing with wickedness

ABSTRACT When addressing problems with wicked tendencies in higher professional education, students experience complexity, uncertainty, and value divergence. Furthermore, they are confronted with disciplinary, organisational, and sector boundaries. Prior research has revealed variability in students’ experiences and boundary-crossing behaviour when dealing with problems with wicked tendencies. In this study we explore these differences by identifying student profiles based on the attributes that comprise the competence for dealing with problems with wicked tendencies, and by identifying their relations with students’ boundary-crossing behaviour and relevant work experience. Person-centred cluster analysis in a sample of first-year students (N = 264) from a bachelor’s programme in social work identified four student profiles, based on students’ self-assessed degree of creativity, critical thinking, initiative, proactivity, risk tolerance, and work efficacy. Meaningful relations with students’ prior work experiences and their boundary-crossing behaviour were found. These profiles could serve to better understand students’ boundary-crossing behaviour when confronted with problems with wicked tendencies, and help teachers foster the development of all students.

Scanpath Prediction on Information Visualisations.

We propose Unified Model of Saliency and Scanpaths (UMSS)- a model that learns to predict multi-duration saliency and scanpaths (i.e. sequences of eye fixations) on information visualisations. Although scanpaths provide rich information about the importance of different visualisation elements during the visual exploration process, prior work has been limited to predicting aggregated attention statistics, such as visual saliency. We present in-depth analyses of gaze behaviour for different information visualisation elements (e.g. Title, Label, Data) on the popular MASSVIS dataset. We show that while, overall, gaze patterns are surprisingly consistent across visualisations and viewers, there are also structural differences in gaze dynamics for different elements. Informed by our analyses, UMSS first predicts multi-duration element-level saliency maps, then probabilistically samples scanpaths from them. Extensive experiments on MASSVIS show that our method consistently outperforms state-of-the-art methods with respect to several, widely used scanpath and saliency evaluation metrics. Our method achieves a relative improvement in sequence score of 11.5% for scanpath prediction, and a relative improvement in Pearson correlation coefficient of up to 23.6% for saliency prediction. These results are auspicious and point towards richer user models and simulations of visual attention on visualisations without the need for any eye tracking equipment.

Occurrent beliefs about emotions predict emotion regulation in everyday life.

Whether and how people regulate their negative emotions matters a great deal. However, it is not yet clear why people regulate as they do. One promising idea is that people's beliefs shape their emotion regulation choices, and initial evidence indicates that individuals' dispositional beliefs about emotions are indeed associated with general patterns of emotion regulation. The present study extends prior work on emotion beliefs to better understand how occurrent (i.e., momentary) beliefs about helpfulness, controllability, and justification of specific emotions shape whether and how people regulate negative emotions in everyday life. Participants (N = 143; U.S. community college students recruited in 2022; 76% female; age = 18-60) completed a 7-day experience-sampling protocol in which they were pinged three times per day to describe their most recent negative experiences and answer questions about their emotions, occurrent beliefs about emotions, and emotion regulation. With respect to whether people regulate their emotions, results reveal that people regulate their emotions more when they perceive them to be less helpful. Exploratory analyses additionally show that people regulate negative emotions more when they perceive them to be more controllable and when emotional intensity is relatively high. In terms of how people regulate their emotions, people are more likely to use reappraisal when emotions are seen as more helpful, more controllable, and less justified; and more likely to use distraction when emotions are seen as less helpful and more justified. These findings contribute to a more fine-grained understanding of how beliefs shape emotion regulation in everyday life.respect to whether people regulate their emotions, results reveal that people regulate their emotions more when they perceive them to be less helpful. Exploratory analyses additionally show that people regulate negative emotions more when they perceive them to be more controllable and when emotional intensity is relatively high. In terms of how people regulate their emotions, people are more likely to use reappraisal when emotions are seen as more helpful, more controllable, and less justified; and more likely to use distraction when emotions are seen as less helpful and more justified. These findings contribute to a more fine-grained understanding of how beliefs shape emotion regulation (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Assessing the Cross-linguistic Utility of Abstract Meaning Representation

Abstract Semantic representations capture the meaning of a text. Abstract Meaning Representation (AMR), a type of semantic representation, focuses on predicate-argument structure and abstracts away from surface form. Though AMR was developed initially for English, it has now been adapted to a multitude of languages in the form of non-English annotation schemas, cross-lingual text-to-AMR parsing, and AMR-to-(non-English) text generation. We advance prior work on cross-lingual AMR by thoroughly investigating the amount, types, and causes of differences that appear in AMRs of different languages. Further, we compare how AMR captures meaning in cross-lingual pairs versus strings, and show that AMR graphs are able to draw out fine-grained differences between parallel sentences. We explore three primary research questions: (1) What are the types and causes of differences in parallel AMRs? (2) How can we measure the amount of difference between AMR pairs in different languages? (3) Given that AMR structure is affected by language and exhibits cross-lingual differences, how do cross-lingual AMR pairs compare to string-based representations of cross-lingual sentence pairs? We find that the source language itself does have a measurable impact on AMR structure, and that translation divergences and annotator choices also lead to differences in cross-lingual AMR pairs. We explore the implications of this finding throughout our study, concluding that, although AMR is useful to capture meaning across languages, evaluations need to take into account source language influences if they are to paint an accurate picture of system output, and meaning generally.

A study of concept similarity in Wikidata

Robust estimation of concept similarity is crucial for applications of AI in the commercial, biomedical, and publishing domains, among others. While the related task of word similarity has been extensively studied, resulting in a wide range of methods, estimating concept similarity between nodes in Wikidata has not been considered so far. In light of the adoption of Wikidata for increasingly complex tasks that rely on similarity, and its unique size, breadth, and crowdsourcing nature, we propose that conceptual similarity should be revisited for the case of Wikidata. In this paper, we study a wide range of representative similarity methods for Wikidata, organized into three categories, and leverage background information for knowledge injection via retrofitting. We measure the impact of retrofitting with different weighted subsets from Wikidata and ProBase. Experiments on three benchmarks show that the best performance is achieved by pairing language models with rich information, whereas the impact of injecting knowledge is most positive on methods that originally do not consider comprehensive information. The performance of retrofitting is conditioned on the selection of high-quality similarity knowledge. A key limitation of this study, similar to prior work lies in the limited size and scope of the similarity benchmarks. While Wikidata provides an unprecedented possibility for a representative evaluation of concept similarity, effectively doing so remains a key challenge.

Try This for Size: Multi-Scale Teleportation in Immersive Virtual Reality.

The ability of a user to adjust their own scale while traveling through virtual environments enables them to inspect tiny features being ant-sized and to gain an overview of the surroundings as a giant. While prior work has almost exclusively focused on steering-based interfaces for multi-scale travel, we present three novel teleportation-based techniques that avoid continuous motion flow to reduce the risk of cybersickness. Our approaches build on the extension of known teleportation workflows and suggest specifying scale adjustments either simultaneously with, as a connected second step after, or separately from the user's new horizontal position. The results of a two-part user study with 30 participants indicate that the simultaneous and connected specification paradigms are both suitable candidates for effective and comfortable multi-scale teleportation with nuanced individual benefits. Scale specification as a separate mode, on the other hand, was considered less beneficial. We compare our findings to prior research and publish the executable of our user study to facilitate replication and further analyses.

Exploring Practical Acoustic Transduction Attacks on Inertial Sensors in MDOF Systems

In cyber-physical systems, inertial sensors are the basis for identifying motion states and making actuation decisions. However, extensive studies have proved the vulnerability of those sensors under acoustic transduction attacks, which leverage malicious acoustics to trigger sensor measurement errors. Unfortunately, the threat from such attacks is not assessed properly because of the incomplete investigation on the attack's potential, especially towards multiple-degree-of-freedom systems, e.g., drones. To thoroughly explore the threat of acoustic transduction attacks, we revisit the attack model and design a new yet practical acoustic modulation-based attack, named KITE. Such an attack enables stable and controllable injections, even under frequency offset based distortions that limit the effect of prior attacking approaches. KITE exploits the potential threat of transduction attacks without the need of strengthening attackers' abilities. Furthermore, we extend the attack surface to multiple-degree-of-freedom (MDOF) systems, which are more widely deployed but ignored by prior work. Our study also covers the scenario of attacking moving targets. By revealing the practical threat from acoustic transduction attacks, we appeal for both the attention to their harm and necessary countermeasures.

IntenSelect+: Enhancing Score-Based Selection in Virtual Reality.

Object selection in virtual environments is one of the most common and recurring interaction tasks. Therefore, the used technique can critically influence a system's overall efficiency and usability. IntenSelect is a scoring-based selection-by-volume technique that was shown to offer improved selection performance over conventional raycasting in virtual reality. This initial method, however, is most pronounced for small spherical objects that converge to a point-like appearance only, is challenging to parameterize, and has inherent limitations in terms of flexibility. We present an enhanced version of IntenSelect called IntenSelect+ designed to overcome multiple shortcomings of the original IntenSelect approach. In an empirical within-subjects user study with 42 participants, we compared IntenSelect+ to IntenSelect and conventional raycasting on various complex object configurations motivated by prior work. In addition to replicating the previously shown benefits of IntenSelect over raycasting, our results demonstrate significant advantages of IntenSelect+ over IntenSelect regarding selection performance, task load, and user experience. We, therefore, conclude that IntenSelect+ is a promising enhancement of the original approach that enables faster, more precise, and more comfortable object selection in immersive virtual environments.

Steady-state Visual Evoked Potentials Reveal Dynamic (Re)allocation of Spatial Attention during Maintenance and Utilization of Visual Working Memory.

Visual working memory (VWM) allows storing goal-relevant information to guide future behavior. Prior work suggests that VWM is spatially organized and relies on spatial attention directed toward locations at which memory items were encoded, even if location is task-irrelevant. Importantly, attention often needs to be dynamically redistributed between locations, for example, in preparation for an upcoming probe. Very little is known about how attentional resources are distributed between multiple locations during a VWM task and even less about the dynamic changes governing such attentional shifts over time. This is largely due to the inability to use behavioral outcomes to reveal fast dynamic changes within trials. We here demonstrated that EEG steady-state visual evoked potentials (SSVEPs) successfully track the dynamic allocation of spatial attention during a VWM task. Participants were presented with to-be-memorized gratings and distractors at two distinct locations, tagged with flickering discs. This allowed us to dynamically track attention allocated to memory and distractor items via their coupling with space by quantifying the amplitude and coherence of SSVEP responses in the EEG signal to flickering stimuli at the former memory and distractor locations. SSVEP responses did not differ between memory and distractor locations during early maintenance. However, shortly before probe comparison, we observed a decrease in SSVEP coherence over distractor locations indicative of a reallocation of spatial attentional resources. RTs were shorter when preceded by stronger decreases in SSVEP coherence at distractor locations, likely reflecting attentional shifts from the distractor to the probe or memory location. We demonstrate that SSVEPs can inform about dynamic processes in VWM, even if location does not have to be reported by participants. This finding not only supports the notion of a spatially organized VWM but also reveals that SSVEPs betray a dynamic prioritization process of working memory items and locations over time that is directly predictive of memory performance.

Investigating the neural basis of schematic false memories by examining schematic and lure pattern similarity

ABSTRACT Schemas allow us to make assumptions about the world based upon previous experiences and aid in memory organisation and retrieval. However, a reliance on schemas may also result in increased false memories to schematically related lures. Prior neuroimaging work has linked schematic processing in memory tasks to activity in prefrontal, visual, and temporal regions. Yet, it is unclear what type of processing in these regions underlies memory errors. The current study examined where schematic lures exhibit greater neural similarity to schematic targets, leading to this memory error, as compared to neural overlap with non-schematic lures, which, like schematic lures, are novel items at retrieval. Results showed that patterns of neural activity in ventromedial prefrontal cortex, medial frontal gyrus, middle temporal gyrus, hippocampus, and occipital cortices exhibited greater neural pattern similarity for schematic targets and schematic lures than between schematic lures and non-schematic lures. As such, results suggest that schematic membership, and not object history, may be more critical to the neural processes underlying memory retrieval in the context of a strong schema.

Calibration of Transformer-Based Models for Identifying Stress and Depression in Social Media

In today’s fast-paced world, the rates of stress and depression present a surge. People use social media for expressing their thoughts and feelings through posts. Therefore, social media provide assistance for the early detection of mental health conditions. Existing methods mainly introduce feature extraction approaches and train shallow machine learning (ML) classifiers. For addressing the need of creating a large feature set and obtaining better performance, other research studies use deep neural networks or language models based on transformers. Despite the fact that transformer-based models achieve noticeable improvements, they cannot often capture rich factual knowledge. Although there have been proposed a number of studies aiming to enhance the pretrained transformer-based models with extra information or additional modalities, no prior work has exploited these modifications for detecting stress and depression through social media. In addition, although the reliability of a machine learning (ML) model’s confidence in its predictions is critical for high-risk applications, there is no prior work taken into consideration the model calibration. To resolve the above issues, we present the first study in the task of depression and stress detection in social media, which injects extra-linguistic information in transformer-based models, namely, bidirectional encoder representations from transformers (BERT) and MentalBERT. Specifically, the proposed approach employs a multimodal adaptation gate for creating the combined embeddings, which are given as input to a BERT (or MentalBERT) model. For taking into account the model calibration, we apply label smoothing. We test our proposed approaches in three publicly available datasets and demonstrate that the integration of linguistic features into transformer-based models presents a surge in performance. Also, the usage of label smoothing contributes to both the improvement of the model’s performance and the calibration of the model. We finally perform a linguistic analysis of the posts and show differences in language between stressful and nonstressful texts, as well as depressive and nondepressive posts.

Dual-promotion: Bragging better by promoting peers.

To create favorable impressions and receive credit, individuals need to share information about their past accomplishments. Broadcasting one's past accomplishments or claiming credit to demonstrate competence, however, can harm perceptions of warmth and likability. In fact, prior work has conceptualized self-promotion as a hydraulic challenge: tactics that boost perceptions along one dimension (e.g., competence) harm perceptions along other dimensions (e.g., warmth). In this work, we identify a novel approach to self-promotion: We show that by combining self-promotion with other-promotion (complimenting or giving credit to others), which we term "dual-promotion," individuals can project both warmth and competence to make better impressions on observers than they do by only self-promoting. In seven preregistered studies, including analyses of annual reports from members of Congress and experiments using social network, workplace, and political contexts (total N = 1,448), we show that individuals who engage in dual-promotion create more favorable impressions of warmth and competence than those who only engage in self-promotion. The beneficial effects of dual-promotion are robust to both competitive and noncompetitive contexts and extend to behavioral intentions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).